





"""simple docstring"""
__UpperCamelCase     :						Union[str, Any]						       =  '''
# Transformers installation
! pip install transformers datasets
# To install from source instead of the last release, comment the command above and uncomment the following one.
# ! pip install git+https://github.com/huggingface/transformers.git
'''

__UpperCamelCase     :						Any						       =  [{'''type''': '''code''', '''content''': INSTALL_CONTENT}]
__UpperCamelCase     :						List[str]						       =  {
    '''{processor_class}''': '''FakeProcessorClass''',
    '''{model_class}''': '''FakeModelClass''',
    '''{object_class}''': '''FakeObjectClass''',
}







"""simple docstring"""



import os
from shutil import copyfile
from typing import List, Optional, Tuple

from tokenizers import processors

from ...tokenization_utils import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
 from .tokenization_mbart import MBartTokenizer
else:
 a       :						Optional[Any]       		=		None


a       :						str       		=		logging.get_logger(__name__)


a       :						str       		=		{'''vocab_file''': '''sentencepiece.bpe.model''', '''tokenizer_file''': '''tokenizer.json'''}

a       :						str       		=		{
    '''vocab_file''': {
        '''facebook/mbart-large-en-ro''': (
            '''https://huggingface.co/facebook/mbart-large-en-ro/resolve/main/sentencepiece.bpe.model'''
        ),
        '''facebook/mbart-large-cc25''': (
            '''https://huggingface.co/facebook/mbart-large-cc25/resolve/main/sentencepiece.bpe.model'''
        ),
    },
    '''tokenizer_file''': {
        '''facebook/mbart-large-en-ro''': '''https://huggingface.co/facebook/mbart-large-en-ro/resolve/main/tokenizer.json''',
        '''facebook/mbart-large-cc25''': '''https://huggingface.co/facebook/mbart-large-cc25/resolve/main/tokenizer.json''',
    },
}

a       :						List[str]       		=		{
    '''facebook/mbart-large-en-ro''': 1_0_2_4,
    '''facebook/mbart-large-cc25''': 1_0_2_4,
}

# fmt: off
a       :						Optional[int]       		=		['''ar_AR''', '''cs_CZ''', '''de_DE''', '''en_XX''', '''es_XX''', '''et_EE''', '''fi_FI''', '''fr_XX''', '''gu_IN''', '''hi_IN''', '''it_IT''', '''ja_XX''', '''kk_KZ''', '''ko_KR''', '''lt_LT''', '''lv_LV''', '''my_MM''', '''ne_NP''', '''nl_XX''', '''ro_RO''', '''ru_RU''', '''si_LK''', '''tr_TR''', '''vi_VN''', '''zh_CN''']




class 		a_    (			_UpperCAmelCase						):
    a   :					Optional[int]			     =					VOCAB_FILES_NAMES
    a   :					List[Any]			     =					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    a   :					Optional[Any]			     =					PRETRAINED_VOCAB_FILES_MAP
    a   :					Optional[int]			     =					['input_ids', 'attention_mask']
    a   :					str			     =					MBartTokenizer

    a   :					List[int]			     =					[]
    a   :					List[int]			     =					[]







    def __init__(      self      :							str  ,       __UpperCamelCase      :							List[str]=None  ,       __UpperCamelCase      :							List[Any]=None  ,       __UpperCamelCase      :							Dict="<s>"  ,       __UpperCamelCase      :							int="</s>"  ,       __UpperCamelCase      :							str="</s>"  ,       __UpperCamelCase      :							Union[str, Any]="<s>"  ,       __UpperCamelCase      :							Any="<unk>"  ,       __UpperCamelCase      :							Any="<pad>"  ,       __UpperCamelCase      :							int="<mask>"  ,       __UpperCamelCase      :							Any=None  ,       __UpperCamelCase      :							Optional[Any]=None  ,       __UpperCamelCase      :							Optional[Any]=None  ,       **__UpperCamelCase      :							Optional[int]  ,       )		->Union[str, Any]:






        '''simple docstring'''






        _UpperCAmelCase        = AddedToken(__UpperCamelCase  ,       lstrip=__UpperCamelCase  ,       rstrip=__UpperCamelCase  ) if isinstance(__UpperCamelCase  ,       __UpperCamelCase  ) else mask_token

        super().__init__(
            vocab_file=__UpperCamelCase  ,       tokenizer_file=__UpperCamelCase  ,       bos_token=__UpperCamelCase  ,       eos_token=__UpperCamelCase  ,       sep_token=__UpperCamelCase  ,       cls_token=__UpperCamelCase  ,       unk_token=__UpperCamelCase  ,       pad_token=__UpperCamelCase  ,       mask_token=__UpperCamelCase  ,       src_lang=__UpperCamelCase  ,       tgt_lang=__UpperCamelCase  ,       additional_special_tokens=__UpperCamelCase  ,       **__UpperCamelCase  ,       )

        _UpperCAmelCase        = vocab_file
        _UpperCAmelCase        = False if not self.vocab_file else True

        _UpperCAmelCase        = FAIRSEQ_LANGUAGE_CODES.copy()

        if additional_special_tokens is not None:
            # Only add those special tokens if they are not already there.
            _additional_special_tokens.extend(
                [t for t in additional_special_tokens if t not in _additional_special_tokens]  )

        self.add_special_tokens({"""additional_special_tokens""": _additional_special_tokens}  )
        _UpperCAmelCase        = {
            lang_code: self.convert_tokens_to_ids(__UpperCamelCase  ) for lang_code in FAIRSEQ_LANGUAGE_CODES
        }

        _UpperCAmelCase        = src_lang if src_lang is not None else """en_XX"""
        _UpperCAmelCase        = self.convert_tokens_to_ids(self._src_lang  )
        _UpperCAmelCase        = tgt_lang
        self.set_src_lang_special_tokens(self._src_lang  )







    @property
    def   _snake_case       (      self      :							Optional[Any]  )		->str:






        '''simple docstring'''






        return self._src_lang







    @src_lang.setter
    def   _snake_case       (      self      :							int  ,       __UpperCamelCase      :							str  )		->None:






        '''simple docstring'''






        _UpperCAmelCase        = new_src_lang
        self.set_src_lang_special_tokens(self._src_lang  )







    def   _snake_case       (      self      :							str  ,       __UpperCamelCase      :							List[int]  ,       __UpperCamelCase      :							Optional[List[int]] = None  )		->List[int]:






        '''simple docstring'''






        if token_ids_a is None:
            return self.prefix_tokens + token_ids_a + self.suffix_tokens
        # We don't expect to process pairs, but leave the pair logic for API consistency
        return self.prefix_tokens + token_ids_a + token_ids_a + self.suffix_tokens







    def   _snake_case       (      self      :							Union[str, Any]  ,       __UpperCamelCase      :							List[int]  ,       __UpperCamelCase      :							Optional[List[int]] = None  )		->List[int]:






        '''simple docstring'''






        _UpperCAmelCase        = [self.sep_token_id]
        _UpperCAmelCase        = [self.cls_token_id]

        if token_ids_a is None:
            return len(cls + token_ids_a + sep  ) * [0]
        return len(cls + token_ids_a + sep + sep + token_ids_a + sep  ) * [0]







    def   _snake_case       (      self      :							Dict  ,       __UpperCamelCase      :							int  ,       __UpperCamelCase      :							str  ,       __UpperCamelCase      :							Optional[str]  ,       __UpperCamelCase      :							Optional[str]  ,       **__UpperCamelCase      :							Any  )		->Dict:






        '''simple docstring'''






        if src_lang is None or tgt_lang is None:
            raise ValueError("""Translation requires a `src_lang` and a `tgt_lang` for this model"""  )
        _UpperCAmelCase        = src_lang
        _UpperCAmelCase        = self(__UpperCamelCase  ,       add_special_tokens=__UpperCamelCase  ,       return_tensors=__UpperCamelCase  ,       **__UpperCamelCase  )
        _UpperCAmelCase        = self.convert_tokens_to_ids(__UpperCamelCase  )
        _UpperCAmelCase        = tgt_lang_id
        return inputs







    def   _snake_case       (      self      :							Tuple  ,       __UpperCamelCase      :							List[str]  ,       __UpperCamelCase      :							str = "en_XX"  ,       __UpperCamelCase      :							Optional[List[str]] = None  ,       __UpperCamelCase      :							str = "ro_RO"  ,       **__UpperCamelCase      :							Union[str, Any]  ,       )		->BatchEncoding:






        '''simple docstring'''






        _UpperCAmelCase        = src_lang
        _UpperCAmelCase        = tgt_lang
        return super().prepare_seqaseq_batch(__UpperCamelCase  ,       __UpperCamelCase  ,       **__UpperCamelCase  )







    def   _snake_case       (      self      :							Union[str, Any]  )		->int:






        '''simple docstring'''






        return self.set_src_lang_special_tokens(self.src_lang  )







    def   _snake_case       (      self      :							Union[str, Any]  )		->Tuple:






        '''simple docstring'''






        return self.set_tgt_lang_special_tokens(self.tgt_lang  )







    def   _snake_case       (      self      :							Union[str, Any]  ,       __UpperCamelCase      :							Dict  )		->None:






        '''simple docstring'''






        _UpperCAmelCase        = self.convert_tokens_to_ids(__UpperCamelCase  )
        _UpperCAmelCase        = []
        _UpperCAmelCase        = [self.eos_token_id, self.cur_lang_code]

        _UpperCAmelCase        = self.convert_ids_to_tokens(self.prefix_tokens  )
        _UpperCAmelCase        = self.convert_ids_to_tokens(self.suffix_tokens  )

        _UpperCAmelCase        = processors.TemplateProcessing(
            single=prefix_tokens_str + ["""$A"""] + suffix_tokens_str  ,       pair=prefix_tokens_str + ["""$A""", """$B"""] + suffix_tokens_str  ,       special_tokens=list(zip(prefix_tokens_str + suffix_tokens_str  ,       self.prefix_tokens + self.suffix_tokens  )  )  ,       )







    def   _snake_case       (      self      :							List[str]  ,       __UpperCamelCase      :							str  )		->None:






        '''simple docstring'''






        _UpperCAmelCase        = self.convert_tokens_to_ids(__UpperCamelCase  )
        _UpperCAmelCase        = []
        _UpperCAmelCase        = [self.eos_token_id, self.cur_lang_code]

        _UpperCAmelCase        = self.convert_ids_to_tokens(self.prefix_tokens  )
        _UpperCAmelCase        = self.convert_ids_to_tokens(self.suffix_tokens  )

        _UpperCAmelCase        = processors.TemplateProcessing(
            single=prefix_tokens_str + ["""$A"""] + suffix_tokens_str  ,       pair=prefix_tokens_str + ["""$A""", """$B"""] + suffix_tokens_str  ,       special_tokens=list(zip(prefix_tokens_str + suffix_tokens_str  ,       self.prefix_tokens + self.suffix_tokens  )  )  ,       )







    def   _snake_case       (      self      :							List[Any]  ,       __UpperCamelCase      :							str  ,       __UpperCamelCase      :							Optional[str] = None  )		->Tuple[str]:






        '''simple docstring'''






        if not self.can_save_slow_tokenizer:
            raise ValueError(
                """Your fast tokenizer does not have the necessary information to save the vocabulary for a slow """
                """tokenizer."""  )

        if not os.path.isdir(__UpperCamelCase  ):
            logger.error(f"""Vocabulary path ({save_directory}) should be a directory."""  )
            return
        _UpperCAmelCase        = os.path.join(
            __UpperCamelCase  ,       (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]  )

        if os.path.abspath(self.vocab_file  ) != os.path.abspath(__UpperCamelCase  ):
            copyfile(self.vocab_file  ,       __UpperCamelCase  )

        return (out_vocab_file,)